标题： 最优控制邂逅流匹配：多被试保真度的合理路径 显示英文标题

标题： Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity

摘要： 文本到图像（T2I）模型在单实体提示上表现优异，但在多主体描述上存在困难，常常出现属性泄露、身份纠缠和主体遗漏。我们引入了第一个理论框架，具有可优化的目标，用于引导采样动态以实现多主体保真度。通过随机最优控制（SOC）来看待流匹配（FM），我们将主体解缠作为对训练好的FM采样器的控制。这产生了两个与架构无关的算法：(i) 一种无需训练的测试时控制器，通过一次传递更新扰动基础速度，以及 (ii) 伴随匹配，一种轻量级微调规则，在保持基础模型能力的同时回归控制网络到反向伴随信号。相同的公式统一了先前的注意力启发式方法，通过流-扩散对应关系扩展到扩散模型，并提供了第一条明确设计用于多主体保真度的微调路径。实证研究表明，在Stable Diffusion 3.5、FLUX和Stable Diffusion XL上，两种算法都能持续提升多主体对齐度，同时保持基础模型风格。测试时控制在商品GPU上运行效率高，有限提示上训练的微调控制器能推广到未见过的提示。我们进一步突出了FOCUS（Flow Optimal Control for Unentangled Subjects），它在各种模型中实现了最先进的多主体保真度。 显示英文摘要

摘要： Text-to-image (T2I) models excel on single-entity prompts but struggle with multi-subject descriptions, often showing attribute leakage, identity entanglement, and subject omissions. We introduce the first theoretical framework with a principled, optimizable objective for steering sampling dynamics toward multi-subject fidelity. Viewing flow matching (FM) through stochastic optimal control (SOC), we formulate subject disentanglement as control over a trained FM sampler. This yields two architecture-agnostic algorithms: (i) a training-free test-time controller that perturbs the base velocity with a single-pass update, and (ii) Adjoint Matching, a lightweight fine-tuning rule that regresses a control network to a backward adjoint signal while preserving base-model capabilities. The same formulation unifies prior attention heuristics, extends to diffusion models via a flow-diffusion correspondence, and provides the first fine-tuning route explicitly designed for multi-subject fidelity. Empirically, on Stable Diffusion 3.5, FLUX, and Stable Diffusion XL, both algorithms consistently improve multi-subject alignment while maintaining base-model style. Test-time control runs efficiently on commodity GPUs, and fine-tuned controllers trained on limited prompts generalize to unseen ones. We further highlight FOCUS (Flow Optimal Control for Unentangled Subjects), which achieves state-of-the-art multi-subject fidelity across models.